import numpy as np
import pandas as pd
import os, gc
from glob import glob
from tqdm import tqdm
from sklearn.preprocessing import MinMaxScaler
from matplotlib import pyplot as plt
import seaborn as sns
sns.set()
import sys
sys.path.append(f'/home/{os.environ.get("USER")}/PythonLibrary')
import EDA
X_train = pd.read_csv('../input/train.csv.zip')
X_train['ind'] = X_train.index
y_train = X_train['target']
X_train = X_train.iloc[:,2:]
X_train_0 = X_train[y_train==0]
X_train_1 = X_train[y_train==1]
X_train_0.var_0.plot(label='target 0', legend=True, alpha=0.9)
X_train_1.var_0.plot(label='target 1', legend=True, alpha=0.9)
M, N = 5, 5
col_list = list( zip(*[iter(X_train_0.columns[:-1])]*(5*5)) )
for col in col_list:
fig, axes = plt.subplots(ncols=M, nrows=N, figsize=(28, 25), sharex=True)
for i,(ax, c) in enumerate(zip(axes.ravel(), col)):
X_train_0[c].plot(ax=ax, title=c, label='target 0', legend=True, alpha=0.9)
X_train_1[c].plot(ax=ax, label='target 1', legend=True, alpha=0.9)
plt.show()